Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations586642
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory44.8 MiB
Average record size in memory80.0 B

Variable types

Numeric7
Text1
Categorical1
DateTime1

Alerts

Data speed is highly overall correlated with Packet speedHigh correlation
Packet speed is highly overall correlated with Data speedHigh correlation
Attack code is highly imbalanced (86.1%) Imbalance
Source IP count is highly skewed (γ1 = 20.64839776) Skewed
Port number has 138720 (23.6%) zeros Zeros
Avg packet len has 95644 (16.3%) zeros Zeros

Reproduction

Analysis started2025-03-20 16:29:00.770159
Analysis finished2025-03-20 16:29:10.936008
Duration10.17 seconds
Software versionydata-profiling vv4.15.0
Download configurationconfig.json

Variables

Attack ID
Real number (ℝ)

Distinct134769
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81774.73
Minimum1
Maximum134769
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2025-03-20T16:29:10.992736image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3884
Q145107.25
median93556
Q3121627
95-th percentile131719
Maximum134769
Range134768
Interquartile range (IQR)76519.75

Descriptive statistics

Standard deviation42440.014
Coefficient of variation (CV)0.5189869
Kurtosis-1.105818
Mean81774.73
Median Absolute Deviation (MAD)32803
Skewness-0.49731565
Sum4.7972491 × 1010
Variance1.8011547 × 109
MonotonicityNot monotonic
2025-03-20T16:29:11.089349image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95972 12534
 
2.1%
84340 6443
 
1.1%
24243 5300
 
0.9%
93249 4995
 
0.9%
126513 3573
 
0.6%
24113 3226
 
0.5%
84337 3155
 
0.5%
3188 2646
 
0.5%
51781 1966
 
0.3%
1043 1885
 
0.3%
Other values (134759) 540919
92.2%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 2
< 0.1%
4 1
< 0.1%
5 2
< 0.1%
6 1
< 0.1%
7 2
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
134769 1
 
< 0.1%
134768 2
< 0.1%
134767 1
 
< 0.1%
134766 1
 
< 0.1%
134765 2
< 0.1%
134764 3
< 0.1%
134763 1
 
< 0.1%
134762 2
< 0.1%
134761 4
< 0.1%
134760 2
< 0.1%

Detect count
Real number (ℝ)

Distinct12534
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean359.30195
Minimum1
Maximum12534
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2025-03-20T16:29:11.176736image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median16
Q3176
95-th percentile1708
Maximum12534
Range12533
Interquartile range (IQR)174

Descriptive statistics

Standard deviation1199.8967
Coefficient of variation (CV)3.3395219
Kurtosis43.35277
Mean359.30195
Median Absolute Deviation (MAD)15
Skewness6.0803895
Sum2.1078162 × 108
Variance1439752.2
MonotonicityNot monotonic
2025-03-20T16:29:11.266973image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 134769
23.0%
2 48500
 
8.3%
3 24559
 
4.2%
4 16201
 
2.8%
5 12135
 
2.1%
6 9842
 
1.7%
7 8205
 
1.4%
8 7050
 
1.2%
9 6115
 
1.0%
10 5328
 
0.9%
Other values (12524) 313938
53.5%
ValueCountFrequency (%)
1 134769
23.0%
2 48500
 
8.3%
3 24559
 
4.2%
4 16201
 
2.8%
5 12135
 
2.1%
6 9842
 
1.7%
7 8205
 
1.4%
8 7050
 
1.2%
9 6115
 
1.0%
10 5328
 
0.9%
ValueCountFrequency (%)
12534 1
< 0.1%
12533 1
< 0.1%
12532 1
< 0.1%
12531 1
< 0.1%
12530 1
< 0.1%
12529 1
< 0.1%
12528 1
< 0.1%
12527 1
< 0.1%
12526 1
< 0.1%
12525 1
< 0.1%
Distinct18200
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
2025-03-20T16:29:11.388791image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length8
Median length7
Mean length7.1429833
Min length7

Characters and Unicode

Total characters4190374
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10068 ?
Unique (%)1.7%

Sample

1st rowIP_0001
2nd rowIP_0002
3rd rowIP_0003
4th rowIP_0003
5th rowIP_0002
ValueCountFrequency (%)
ip_0151 134155
22.9%
ip_0040 21553
 
3.7%
ip_0202 21322
 
3.6%
ip_0006 19880
 
3.4%
ip_0010 18257
 
3.1%
ip_0017 16867
 
2.9%
ip_0074 16759
 
2.9%
ip_0024 15194
 
2.6%
ip_0965 13592
 
2.3%
ip_15194 12608
 
2.1%
Other values (18190) 296455
50.5%
2025-03-20T16:29:11.588058image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 729485
17.4%
I 586642
14.0%
P 586642
14.0%
_ 586642
14.0%
1 516352
12.3%
5 294489
7.0%
2 178133
 
4.3%
4 167082
 
4.0%
6 137821
 
3.3%
9 116564
 
2.8%
Other values (3) 290522
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4190374
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 729485
17.4%
I 586642
14.0%
P 586642
14.0%
_ 586642
14.0%
1 516352
12.3%
5 294489
7.0%
2 178133
 
4.3%
4 167082
 
4.0%
6 137821
 
3.3%
9 116564
 
2.8%
Other values (3) 290522
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4190374
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 729485
17.4%
I 586642
14.0%
P 586642
14.0%
_ 586642
14.0%
1 516352
12.3%
5 294489
7.0%
2 178133
 
4.3%
4 167082
 
4.0%
6 137821
 
3.3%
9 116564
 
2.8%
Other values (3) 290522
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4190374
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 729485
17.4%
I 586642
14.0%
P 586642
14.0%
_ 586642
14.0%
1 516352
12.3%
5 294489
7.0%
2 178133
 
4.3%
4 167082
 
4.0%
6 137821
 
3.3%
9 116564
 
2.8%
Other values (3) 290522
 
6.9%

Port number
Real number (ℝ)

Zeros 

Distinct34137
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25066.609
Minimum0
Maximum65535
Zeros138720
Zeros (%)23.6%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2025-03-20T16:29:11.680586image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q153
median6881
Q351413
95-th percentile61994
Maximum65535
Range65535
Interquartile range (IQR)51360

Descriptive statistics

Standard deviation25775.209
Coefficient of variation (CV)1.0282687
Kurtosis-1.7678858
Mean25066.609
Median Absolute Deviation (MAD)6881
Skewness0.25110692
Sum1.4705126 × 1010
Variance6.6436138 × 108
MonotonicityNot monotonic
2025-03-20T16:29:11.766233image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 138720
23.6%
4500 53741
 
9.2%
49261 53365
 
9.1%
443 43330
 
7.4%
51413 22287
 
3.8%
80 19350
 
3.3%
60645 18397
 
3.1%
53 16013
 
2.7%
22000 7024
 
1.2%
63396 4925
 
0.8%
Other values (34127) 209490
35.7%
ValueCountFrequency (%)
0 138720
23.6%
1 1
 
< 0.1%
12 36
 
< 0.1%
20 24
 
< 0.1%
22 2876
 
0.5%
23 20
 
< 0.1%
25 1146
 
0.2%
53 16013
 
2.7%
68 1
 
< 0.1%
80 19350
 
3.3%
ValueCountFrequency (%)
65535 19
< 0.1%
65534 2
 
< 0.1%
65533 4
 
< 0.1%
65532 3
 
< 0.1%
65531 7
 
< 0.1%
65529 4
 
< 0.1%
65528 5
 
< 0.1%
65527 1
 
< 0.1%
65526 3
 
< 0.1%
65525 13
< 0.1%

Attack code
Categorical

Imbalance 

Distinct48
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
High volume traffic
520092 
Suspicious traffic
 
38425
Generic UDP
 
7239
Suspicious traffic, CLDAP
 
5000
Suspicious traffic, DNS
 
4810
Other values (43)
 
11076

Length

Max length57
Median length19
Mean length18.945058
Min length3

Characters and Unicode

Total characters11113967
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowHigh volume traffic
2nd rowHigh volume traffic
3rd rowHigh volume traffic
4th rowHigh volume traffic
5th rowHigh volume traffic

Common Values

ValueCountFrequency (%)
High volume traffic 520092
88.7%
Suspicious traffic 38425
 
6.5%
Generic UDP 7239
 
1.2%
Suspicious traffic, CLDAP 5000
 
0.9%
Suspicious traffic, DNS 4810
 
0.8%
CLDAP, High volume traffic 4308
 
0.7%
NTP 1175
 
0.2%
Suspicious traffic, NTP 1020
 
0.2%
DNS, High volume traffic 1015
 
0.2%
CLDAP 708
 
0.1%
Other values (38) 2850
 
0.5%

Length

2025-03-20T16:29:11.856175image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
traffic 576264
33.6%
high 526205
30.7%
volume 526205
30.7%
suspicious 50059
 
2.9%
cldap 10658
 
0.6%
dns 7522
 
0.4%
generic 7240
 
0.4%
udp 7240
 
0.4%
ntp 2481
 
0.1%
ssdp 445
 
< 0.1%
Other values (13) 1900
 
0.1%

Most occurring characters

ValueCountFrequency (%)
i 1210179
 
10.9%
f 1152875
 
10.4%
1129577
 
10.2%
c 633979
 
5.7%
u 626323
 
5.6%
r 583851
 
5.3%
t 577795
 
5.2%
a 577386
 
5.2%
o 576641
 
5.2%
e 541042
 
4.9%
Other values (29) 3504319
31.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11113967
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1210179
 
10.9%
f 1152875
 
10.4%
1129577
 
10.2%
c 633979
 
5.7%
u 626323
 
5.6%
r 583851
 
5.3%
t 577795
 
5.2%
a 577386
 
5.2%
o 576641
 
5.2%
e 541042
 
4.9%
Other values (29) 3504319
31.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11113967
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1210179
 
10.9%
f 1152875
 
10.4%
1129577
 
10.2%
c 633979
 
5.7%
u 626323
 
5.6%
r 583851
 
5.3%
t 577795
 
5.2%
a 577386
 
5.2%
o 576641
 
5.2%
e 541042
 
4.9%
Other values (29) 3504319
31.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11113967
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1210179
 
10.9%
f 1152875
 
10.4%
1129577
 
10.2%
c 633979
 
5.7%
u 626323
 
5.6%
r 583851
 
5.3%
t 577795
 
5.2%
a 577386
 
5.2%
o 576641
 
5.2%
e 541042
 
4.9%
Other values (29) 3504319
31.5%

Packet speed
Real number (ℝ)

High correlation 

Distinct6173
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78997.206
Minimum10500
Maximum3906400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2025-03-20T16:29:11.937147image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum10500
5-th percentile51300
Q156800
median64500
Q377300
95-th percentile140100
Maximum3906400
Range3895900
Interquartile range (IQR)20500

Descriptive statistics

Standard deviation84232.366
Coefficient of variation (CV)1.0662702
Kurtosis425.22554
Mean78997.206
Median Absolute Deviation (MAD)9100
Skewness17.076545
Sum4.6343079 × 1010
Variance7.0950915 × 109
MonotonicityNot monotonic
2025-03-20T16:29:12.032085image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51600 2731
 
0.5%
51000 2720
 
0.5%
50400 2651
 
0.5%
51300 2651
 
0.5%
51900 2629
 
0.4%
54000 2619
 
0.4%
52200 2606
 
0.4%
56400 2563
 
0.4%
52800 2528
 
0.4%
52500 2489
 
0.4%
Other values (6163) 560455
95.5%
ValueCountFrequency (%)
10500 1
< 0.1%
10600 1
< 0.1%
11800 1
< 0.1%
12600 1
< 0.1%
12800 1
< 0.1%
13100 1
< 0.1%
13900 1
< 0.1%
14000 1
< 0.1%
14100 1
< 0.1%
14300 2
< 0.1%
ValueCountFrequency (%)
3906400 1
< 0.1%
3905500 1
< 0.1%
3882900 1
< 0.1%
3850000 1
< 0.1%
3763300 1
< 0.1%
3761200 1
< 0.1%
3731900 1
< 0.1%
3684100 1
< 0.1%
3677400 1
< 0.1%
3314900 1
< 0.1%

Data speed
Real number (ℝ)

High correlation 

Distinct1523
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.254136
Minimum0
Maximum2744
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2025-03-20T16:29:12.120153image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q168
median80
Q396
95-th percentile146
Maximum2744
Range2744
Interquartile range (IQR)28

Descriptive statistics

Standard deviation83.816683
Coefficient of variation (CV)0.96060412
Kurtosis270.61114
Mean87.254136
Median Absolute Deviation (MAD)14
Skewness13.279122
Sum51186941
Variance7025.2363
MonotonicityNot monotonic
2025-03-20T16:29:12.205382image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72 15894
 
2.7%
73 15192
 
2.6%
74 14327
 
2.4%
75 13506
 
2.3%
76 13196
 
2.2%
77 12790
 
2.2%
78 12456
 
2.1%
71 12032
 
2.1%
5 12002
 
2.0%
79 11922
 
2.0%
Other values (1513) 453325
77.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 1
 
< 0.1%
2 102
 
< 0.1%
3 5113
0.9%
4 8296
1.4%
5 12002
2.0%
6 10238
1.7%
7 4738
 
0.8%
8 1274
 
0.2%
9 731
 
0.1%
ValueCountFrequency (%)
2744 1
< 0.1%
2711 1
< 0.1%
2704 1
< 0.1%
2691 1
< 0.1%
2685 1
< 0.1%
2678 1
< 0.1%
2657 1
< 0.1%
2644 1
< 0.1%
2641 2
< 0.1%
2635 1
< 0.1%

Avg packet len
Real number (ℝ)

Zeros 

Distinct1447
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean974.81376
Minimum0
Maximum1518
Zeros95644
Zeros (%)16.3%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2025-03-20T16:29:12.290544image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1594
median1263
Q31439
95-th percentile1498
Maximum1518
Range1518
Interquartile range (IQR)845

Descriptive statistics

Standard deviation567.81661
Coefficient of variation (CV)0.58248727
Kurtosis-0.93527468
Mean974.81376
Median Absolute Deviation (MAD)213
Skewness-0.8574472
Sum5.718667 × 108
Variance322415.7
MonotonicityNot monotonic
2025-03-20T16:29:12.378725image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 95644
 
16.3%
66 14588
 
2.5%
1064 14206
 
2.4%
780 13955
 
2.4%
1028 13406
 
2.3%
1506 11509
 
2.0%
1296 9260
 
1.6%
1475 8245
 
1.4%
1486 7545
 
1.3%
1498 6793
 
1.2%
Other values (1437) 391491
66.7%
ValueCountFrequency (%)
0 95644
16.3%
21 1
 
< 0.1%
47 2
 
< 0.1%
50 1
 
< 0.1%
51 14
 
< 0.1%
52 12
 
< 0.1%
57 1
 
< 0.1%
63 2
 
< 0.1%
65 34
 
< 0.1%
66 14588
 
2.5%
ValueCountFrequency (%)
1518 5379
0.9%
1517 126
 
< 0.1%
1516 12
 
< 0.1%
1515 20
 
< 0.1%
1514 21
 
< 0.1%
1513 22
 
< 0.1%
1512 282
 
< 0.1%
1511 11
 
< 0.1%
1510 244
 
< 0.1%
1509 28
 
< 0.1%

Source IP count
Real number (ℝ)

Skewed 

Distinct2658
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.902049
Minimum0
Maximum11557
Zeros13
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2025-03-20T16:29:12.465816image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q36
95-th percentile158
Maximum11557
Range11557
Interquartile range (IQR)5

Descriptive statistics

Standard deviation258.85221
Coefficient of variation (CV)7.0145754
Kurtosis561.67489
Mean36.902049
Median Absolute Deviation (MAD)1
Skewness20.648398
Sum21648292
Variance67004.466
MonotonicityNot monotonic
2025-03-20T16:29:12.555998image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 265496
45.3%
2 58781
 
10.0%
3 45778
 
7.8%
4 36755
 
6.3%
5 29168
 
5.0%
6 22546
 
3.8%
7 17169
 
2.9%
8 13140
 
2.2%
9 10123
 
1.7%
10 8222
 
1.4%
Other values (2648) 79464
 
13.5%
ValueCountFrequency (%)
0 13
 
< 0.1%
1 265496
45.3%
2 58781
 
10.0%
3 45778
 
7.8%
4 36755
 
6.3%
5 29168
 
5.0%
6 22546
 
3.8%
7 17169
 
2.9%
8 13140
 
2.2%
9 10123
 
1.7%
ValueCountFrequency (%)
11557 1
< 0.1%
11458 1
< 0.1%
10769 1
< 0.1%
10696 1
< 0.1%
10569 1
< 0.1%
10561 1
< 0.1%
10263 1
< 0.1%
10227 1
< 0.1%
10214 1
< 0.1%
10150 1
< 0.1%

Time
Date

Distinct528045
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
Minimum2022-08-08 18:09:36
Maximum2023-04-27 12:36:44
Invalid dates0
Invalid dates (%)0.0%
2025-03-20T16:29:12.763206image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:12.849920image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2025-03-20T16:29:09.446237image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:05.843229image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:06.427018image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:07.092190image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:07.675070image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:08.273975image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:08.852148image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:09.530133image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:05.928121image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:06.506499image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:07.175572image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:07.761706image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:08.357539image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:08.939451image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:09.609811image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:06.013157image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:06.583825image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:07.255300image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:07.846949image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:08.440456image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:09.023530image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:09.691820image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:06.096578image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:06.665393image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:07.338335image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:07.929845image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:08.521518image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:09.109121image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:09.779814image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:06.183030image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:06.750044image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:07.427004image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:08.017862image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:08.605723image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:09.199263image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:09.858559image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:06.261949image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:06.931118image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:07.507956image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:08.100829image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:08.687893image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:09.278317image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:09.939446image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:06.346195image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:07.011851image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:07.592921image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:08.187180image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:08.771422image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-20T16:29:09.363790image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Correlations

2025-03-20T16:29:12.909124image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Attack IDAttack codeAvg packet lenData speedDetect countPacket speedPort numberSource IP count
Attack ID1.0000.2420.312-0.0800.280-0.205-0.2360.293
Attack code0.2421.0000.3340.4400.1080.4520.1250.430
Avg packet len0.3120.3341.0000.1950.106-0.190-0.0450.055
Data speed-0.0800.4400.1951.000-0.1830.6370.138-0.137
Detect count0.2800.1080.106-0.1831.000-0.046-0.1950.295
Packet speed-0.2050.452-0.1900.637-0.0461.0000.2130.052
Port number-0.2360.125-0.0450.138-0.1950.2131.000-0.072
Source IP count0.2930.4300.055-0.1370.2950.052-0.0721.000

Missing values

2025-03-20T16:29:10.046789image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-20T16:29:10.457020image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Attack IDDetect countVictim IPPort numberAttack codePacket speedData speedAvg packet lenSource IP countTime
011IP_00014500High volume traffic5560073138362022-08-08 18:09:36
121IP_00024500High volume traffic6350090150612022-08-08 18:37:28
231IP_00031200High volume traffic5970079139912022-08-08 18:41:25
332IP_00031200High volume traffic6570086139912022-08-08 18:41:26
441IP_00024500High volume traffic5950085148612022-08-08 18:47:49
551IP_000412347High volume traffic74800108151812022-08-08 18:57:15
652IP_000412347High volume traffic81700118151812022-08-08 18:58:11
761IP_000545574Suspicious traffic891002124612022-08-08 19:09:29
871IP_00014500High volume traffic6110081140432022-08-08 19:11:36
972IP_00014500High volume traffic75400100138632022-08-08 19:11:37
Attack IDDetect countVictim IPPort numberAttack codePacket speedData speedAvg packet lenSource IP countTime
5866321347641IP_001060513High volume traffic6000075132042023-04-27 12:31:05
5866331347642IP_00100High volume traffic5340067132252023-04-27 12:31:06
5866341347651IP_002348529High volume traffic7310085118522023-04-27 12:31:10
5866351347661IP_178270High volume traffic5220074150612023-04-27 12:31:20
5866361347652IP_002361167High volume traffic6000067125752023-04-27 12:32:28
5866371347671IP_174660High volume traffic5130064132512023-04-27 12:32:29
5866381347681IP_01574631High volume traffic6900083126612023-04-27 12:32:51
5866391347682IP_01574631High volume traffic5420065126812023-04-27 12:32:52
5866401347643IP_001052288High volume traffic6970088131872023-04-27 12:35:18
5866411347691IP_004060339High volume traffic6050073127832023-04-27 12:36:44